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How to Build a Hybrid IT Support Model That Actually Works

If you run IT for a digital-first or retail-heavy brand, your world blends HQ users, remote staff, and store or warehouse locations with physical technology in the field. You need reliability without ballooning headcount and predictable speed without late-night heroics. Use this concrete plan to stand up a co-managed operating model. You will see who owns what, how work flows, and how to measure outcomes from day one. The timing is right because IT services spending keeps climbing while leaders sort out what stays in-house versus what goes to partners.

6 Common Factors That Influence Fleet Safety Program Success

Building a safer fleet is not about one silver bullet. It is a set of practical choices that add up, day after day, until safer habits and smarter tools become the way you operate. This article breaks the work into six factors you can act on. Each one is designed to be simple to start, measurable to manage, and durable enough to last when operations get busy.

Fix bugs faster with CircleCI's Chunk AI agent

Bugs hide in plain sight. A date validator that rejects February 29th on leap years. An edge case that slips through code review. A flaky test that passes locally but fails in CI. These issues erode trust in your codebase and waste hours of debugging time. In the era of AI-assisted development, code is being written faster than ever. But speed creates risk.

Boost your test coverage with CircleCI Chunk AI agent

Test coverage is one of those metrics everyone agrees matters until it’s time to actually write the tests. Between shipping features, fixing bugs, and handling production issues, writing comprehensive tests for edge cases and error paths often falls to the bottom of the backlog. The result is coverage gaps that accumulate technical debt and leave your codebase vulnerable to regressions. As AI-powered development tools reshape how we write code, the volume and velocity of changes is accelerating.

How Cisco Revolutionized Platform Engineering with Komodor's Agentic AI

In the world of cloud-native infrastructure, complexity is the silent killer of innovation. For Cisco Outshift, the company’s incubation engine, managing a sprawling environment of AWS EKS clusters and edge-based MicroK8s workloads created a classic bottleneck: the Platform Engineering team was drowning in toil. Facing SRE burnout and the limits of human scaling, Cisco embarked on an ambitious journey to evolve its internal operations from standard DevOps to Agentic AI.

Scaling AI Reliability: Real world lessons from Mistral AI

How does one of the world's leading AI companies keep its infrastructure reliable while shipping new models constantly? In this webinar, Devon Mizelle, Senior SRE at Mistral AI, shares the real story. Devon walks through how Mistral built an automated system that generates synthetic checks for every model the moment it goes live—no manual configuration, no forgotten monitors, no inconsistent alerting. Using monitoring as code, his team eliminated the toil of maintaining hundreds of checks across a rapidly evolving model ecosystem.